A python library to run metal compute kernels on macOS >= 11
Install latest stable release from PyPI:
> python3 -m pip install metalcompute
Install latest unstable version from Github:
> python3 -m pip install git+https://github.com/baldand/py-metal-compute.git
Install locally from source:
> python3 -m pip install .
Example execution from M1-based Mac running macOS 12:
> python3 tests/basic.py
Calculating sin of 1234567 values
Expected value: 0.9805107116699219 Received value: 0.9807852506637573
Metal compute took: 0.0040209293365478516 s
Reference compute took: 0.1068720817565918 s
import metalcompute as mc
devices = mc.get_devices()
# Get list of available Metal devices
dev = mc.Device()
# Call before use. Will open default Metal device
# or to pick a specific device:
# mc.Device(device_index)
program = """
#include <metal_stdlib>
using namespace metal;
kernel void test(const device float *in [[ buffer(0) ]],
device float *out [[ buffer(1) ]],
uint id [[ thread_position_in_grid ]]) {
out[id] = sin(in[id]);
}
"""
function_name = "test"
kernel_fn = dev.kernel(program).function(function_name)
# Will raise exception with details if metal kernel has errors
buf_0 = array('f',[1.0,3.14159]) # Any python buffer object
buf_n = dev.buffer(out_size)
# Allocate metal buffers for input and output (must be compatible with kernel)
# Input buffers can be dev.buffer or python buffers (will be copied)
# Output buffers must be dev.buffer
# Buffer objects support python buffer protocol
# Can be modified or read using e.g. memoryview, numpy.frombuffer
kernel_fn(kernel_call_count, buf_0, ..., buf_n)
# Run the kernel once with supplied input data,
# filling supplied output data
# Specify number of kernel calls
# Will block until data available
handle = kernel_fn(kernel_call_count, buf_0, ..., buf_n)
# Run the kernel once,
# Specify number of kernel calls
# Supply all needed buffers
# Will return immediately, before kernel runs,
# allowing additional kernels to be queued
# Do not modify or read buffers until kernel completed!
del handle
# Block until previously queued kernel has completed
> metalcompute-measure
Using device: Apple M1 (unified memory=True)
Running compute intensive Metal kernel to measure TFLOPS...
Estimated GPU TFLOPS: 2.53236
Running compute intensive Metal kernel to measure data transfer rate...
Data transfer rate: 58.7291 GB/s
# Usage: metalcompute-raymarch [-width <width>] [-height <height>] [-outname <output image file: PNG, JPG>]
> metalcompute-raymarch.py -width 1024 -height 1024 -outname raymarch.jpg
Render took 0.0119569s
# Usage: metalcompute-mandelbrot [-width <width>] [-height <height>] [-outname <output image file: PNG, JPG>]
> metalcompute-mandelbrot
Rendering mandelbrot set using Metal compute, res:4096x4096, iters:8192
Render took 0.401446s
Writing image to mandelbrot.png
Image encoding took 1.35182s
There is an example script to allow livecoding of visual metal kernels entirely within VSCode using a localhost http server to render frames.
It also includes syntax error highlighting in the editor.
See livemetal.py
This is a preview version.